rm(list=ls())library("readxl")
library(quantmod)
library(dplyr)
library(ggplot2)
#library(tidyr)The file below was downloaded 26/10/2020 from the TSX website above. File contains first some rows with non-relevant information to skip
issuers <- read_excel(paste("../../../data_tmp/tsx-andamp-tsxv-listed",
"-companies-2020-10-16-en-1.xlsx", sep=""),
sheet=2, skip=6)
-
/
colnames(issuers) <- lapply(str_replace_all(colnames(issuers), "[\r\n -$\\(\\)\\-]", "_"), FUN=tolower)issuers <- issuers %>% filter(real_estate_sub_sector !=
"Real Estate Operating Companies")
real_estate <- filter(issuers, !is.na(issuers[, 'real_estate_sub_sector']))[, c("real_estate_sub_sector", "name", "root__ticker", "market_cap__c____30_september_2020")]head(real_estate)get_stockdata <- function(stock_name_root) {
out <- try ({
stock_name <- paste(stock_name_root, "-UN.TO", sep="")
stock <- getSymbols(stock_name, src = "yahoo", auto.assign = FALSE)
# calculate monthly returns instead of using all stock data
stock = monthlyReturn(stock)
colnames(stock) <- sapply(colnames(stock), function (colstock) str_split(colstock, "\\.")[[1]][length(str_split(colstock, "\\.")[[1]])])
stock <- data.frame(date=index(stock), coredata(stock))
stock$stock_name <- rep(stock_name, nrow(stock))
stock$stock_name_root <- rep(stock_name_root, nrow(stock))
stock
})
# if stock is not available as "[stockname]-UN.TO", try "[stockname"].TO"
if (class(out) == "try-error") {
out <- try ({
stock_name <- paste(stock_name_root, ".TO", sep="")
stock <- getSymbols(stock_name, src = "yahoo", auto.assign = FALSE)
# calculate monthly returns instead of using all stock data
stock = monthlyReturn(stock)
colnames(stock) <- sapply(colnames(stock), function (colstock) str_split(colstock, "\\.")[[1]][length(str_split(colstock, "\\.")[[1]])])
stock <- data.frame(date=index(stock), coredata(stock))
stock$stock_name <- rep(stock_name, nrow(stock))
stock$stock_name_root <- rep(stock_name_root, nrow(stock))
stock
})
}
out
}start.time <- Sys.time()
get_stock_series <- lapply(real_estate$root__ticker, get_stockdata)AP-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataHOT-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataAX-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataAPR-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataBEI-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataBRE-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "BRE-UN.TO", env = <environment>, :
Unable to import “BRE-UN.TO”.
BRE-UN.TO download failed after two attempts. Error message:
HTTP error 404.
BRE.TO download failed; trying again.BPO-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "BPO-UN.TO", env = <environment>, :
Unable to import “BPO-UN.TO”.
BPO-UN.TO download failed after two attempts. Error message:
HTTP error 404.
BPO.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "BPO.TO", env = <environment>, verbose = FALSE, :
Unable to import “BPO.TO”.
BPO.TO download failed after two attempts. Error message:
HTTP error 404.
BPY-UN.TO download failed; trying again.BPY-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataHOM-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataBTB-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataCAR-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataCSH-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataCHP-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataCUF-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataCRR-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataCRT-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataDRA-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataDIR-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataWarning in for (i in seq_along(specs)) { :
closing unused connection 10 (https://query2.finance.yahoo.com/v7/finance/download/BPY-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=0ZaCTBmwUIT)
Warning in for (i in seq_along(specs)) { :
closing unused connection 9 (https://query2.finance.yahoo.com/v7/finance/download/BPO.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=0ZaCTBmwUIT)
Warning in for (i in seq_along(specs)) { :
closing unused connection 8 (https://query1.finance.yahoo.com/v7/finance/download/BPO.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=5lX0EHTTCJ5)
Warning in for (i in seq_along(specs)) { :
closing unused connection 7 (https://query2.finance.yahoo.com/v7/finance/download/BPO-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=5lX0EHTTCJ5)
Warning in for (i in seq_along(specs)) { :
closing unused connection 6 (https://query1.finance.yahoo.com/v7/finance/download/BPO-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=ECXbPudmyxB)
Warning in for (i in seq_along(specs)) { :
closing unused connection 5 (https://query1.finance.yahoo.com/v7/finance/download/BRE.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=mhkqOB7.Lhz)
Warning in for (i in seq_along(specs)) { :
closing unused connection 4 (https://query1.finance.yahoo.com/v7/finance/download/BRE-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=mhkqOB7.Lhz)
Warning in for (i in seq_along(specs)) { :
closing unused connection 3 (https://query1.finance.yahoo.com/v7/finance/download/BRE-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=X/RIh7lRHJt)
D-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataDRM-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "DRM-UN.TO", env = <environment>, :
Unable to import “DRM-UN.TO”.
DRM-UN.TO download failed after two attempts. Error message:
HTTP error 404.
DRM.TO download failed; trying again.ERE-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "ERE-UN.TO", env = <environment>, :
Unable to import “ERE-UN.TO”.
ERE-UN.TO download failed after two attempts. Error message:
HTTP error 404.
ERE.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "ERE.TO", env = <environment>, verbose = FALSE, :
Unable to import “ERE.TO”.
ERE.TO download failed after two attempts. Error message:
HTTP error 404.
EXE-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "EXE-UN.TO", env = <environment>, :
Unable to import “EXE-UN.TO”.
EXE-UN.TO download failed after two attempts. Error message:
HTTP error 404.
EXE.TO download failed; trying again.FCR-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataFSV-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "FSV-UN.TO", env = <environment>, :
Unable to import “FSV-UN.TO”.
FSV-UN.TO download failed after two attempts. Error message:
HTTP error 404.
FSV.TO download failed; trying again.GDC-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "GDC-UN.TO", env = <environment>, :
Unable to import “GDC-UN.TO”.
GDC-UN.TO download failed after two attempts. Error message:
HTTP error 404.
GDC.TO download failed; trying again.HR-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataINO-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataIIP-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataIVQ-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "IVQ-UN.TO", env = <environment>, :
Unable to import “IVQ-UN.TO”.
IVQ-UN.TO download failed after two attempts. Error message:
HTTP error 404.
IVQ.TO download failed; trying again.DR-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "DR-UN.TO", env = <environment>, verbose = FALSE, :
Unable to import “DR-UN.TO”.
DR-UN.TO download failed after two attempts. Error message:
HTTP error 404.
DR.TO download failed; trying again.MRD-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "MRD-UN.TO", env = <environment>, :
Unable to import “MRD-UN.TO”.
MRD-UN.TO download failed after two attempts. Error message:
HTTP error 404.
MRD.TO download failed; trying again.MR-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataMI-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataMRC-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "MRC-UN.TO", env = <environment>, :
Unable to import “MRC-UN.TO”.
MRC-UN.TO download failed after two attempts. Error message:
HTTP error 404.
MRC.TO download failed; trying again.MRG-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataWarning in for (i in (1L:cols)[do]) { :
closing unused connection 30 (https://query1.finance.yahoo.com/v7/finance/download/MRC.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=0GiFxYmxEkw)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 29 (https://query1.finance.yahoo.com/v7/finance/download/MRC-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=0GiFxYmxEkw)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 28 (https://query1.finance.yahoo.com/v7/finance/download/MRC-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=zMcc.8LskN9)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 27 (https://query2.finance.yahoo.com/v7/finance/download/MRD.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=aaFMiwLkm1s)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 26 (https://query2.finance.yahoo.com/v7/finance/download/MRD-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=aaFMiwLkm1s)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 25 (https://query1.finance.yahoo.com/v7/finance/download/MRD-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=CmtUHRy.Ydp)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 24 (https://query1.finance.yahoo.com/v7/finance/download/DR.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=g/R3XudJb6K)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 23 (https://query2.finance.yahoo.com/v7/finance/download/DR-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=g/R3XudJb6K)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 22 (https://query1.finance.yahoo.com/v7/finance/download/DR-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=d./AWL6Hl76)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 21 (https://query1.finance.yahoo.com/v7/finance/download/IVQ.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=fEhNP1RI7NY)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 20 (https://query1.finance.yahoo.com/v7/finance/download/IVQ-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=fEhNP1RI7NY)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 19 (https://query1.finance.yahoo.com/v7/finance/download/IVQ-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=8502sNx8Cz7)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 18 (https://query2.finance.yahoo.com/v7/finance/download/GDC.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=/dexIhqywnE)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 17 (https://query2.finance.yahoo.com/v7/finance/download/GDC-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=/dexIhqywnE)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 16 (https://query2.finance.yahoo.com/v7/finance/download/GDC-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=8iVOnZKZ6K2)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 15 (https://query2.finance.yahoo.com/v7/finance/download/FSV.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=DIb5a5EXjF8)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 14 (https://query2.finance.yahoo.com/v7/finance/download/FSV-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=DIb5a5EXjF8)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 13 (https://query1.finance.yahoo.com/v7/finance/download/FSV-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=./UknJ2Aonl)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 12 (https://query1.finance.yahoo.com/v7/finance/download/EXE.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=m9qQuPX5590)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 11 (https://query1.finance.yahoo.com/v7/finance/download/EXE-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=m9qQuPX5590)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 10 (https://query2.finance.yahoo.com/v7/finance/download/EXE-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=/6U1XInyc2o)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 9 (https://query1.finance.yahoo.com/v7/finance/download/ERE.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=/6U1XInyc2o)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 8 (https://query2.finance.yahoo.com/v7/finance/download/ERE.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=A3KYuYaARSl)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 7 (https://query2.finance.yahoo.com/v7/finance/download/ERE-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=A3KYuYaARSl)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 6 (https://query2.finance.yahoo.com/v7/finance/download/ERE-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=jIi9hGssuqJ)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 5 (https://query2.finance.yahoo.com/v7/finance/download/DRM.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=WCJwgsuLizu)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 4 (https://query2.finance.yahoo.com/v7/finance/download/DRM-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=WCJwgsuLizu)
Warning in for (i in (1L:cols)[do]) { :
closing unused connection 3 (https://query2.finance.yahoo.com/v7/finance/download/DRM-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=dC4DdeZwFxR)
MRT-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataNVU-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataNWH-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataPLZ-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataPRV-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataREI-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataSIA-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "SIA-UN.TO", env = <environment>, :
Unable to import “SIA-UN.TO”.
SIA-UN.TO download failed after two attempts. Error message:
HTTP error 404.
SIA.TO download failed; trying again.SIA.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataSGR-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "SGR-UN.TO", env = <environment>, :
Unable to import “SGR-UN.TO”.
SGR-UN.TO download failed after two attempts. Error message:
HTTP error 404.
SGR.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "SGR.TO", env = <environment>, verbose = FALSE, :
Unable to import “SGR.TO”.
SGR.TO download failed after two attempts. Error message:
HTTP error 404.
SOT-UN.TO download failed; trying again.SOT-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataSRU-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataSMU-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataTNT-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataWFC-UN.TO download failed; trying again.Error in getSymbols.yahoo(Symbols = "WFC-UN.TO", env = <environment>, :
Unable to import “WFC-UN.TO”.
WFC-UN.TO download failed after two attempts. Error message:
HTTP error 404.
WFC.TO download failed; trying again.WIR-UN.TO contains missing values. Some functions will not work if objects contain missing values in the middle of the series. Consider using na.omit(), na.approx(), na.fill(), etc to remove or replace them.missing values removed from dataWarning in for (i in seq_len(n)) { :
closing unused connection 13 (https://query2.finance.yahoo.com/v7/finance/download/WFC.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=MKtdgATEPNQ)
Warning in for (i in seq_len(n)) { :
closing unused connection 12 (https://query2.finance.yahoo.com/v7/finance/download/WFC-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=MKtdgATEPNQ)
Warning in for (i in seq_len(n)) { :
closing unused connection 11 (https://query2.finance.yahoo.com/v7/finance/download/WFC-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=R79T334MbmJ)
Warning in for (i in seq_len(n)) { :
closing unused connection 10 (https://query2.finance.yahoo.com/v7/finance/download/SOT-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=JO0xh0sfLAF)
Warning in for (i in seq_len(n)) { :
closing unused connection 9 (https://query1.finance.yahoo.com/v7/finance/download/SGR.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=JO0xh0sfLAF)
Warning in for (i in seq_len(n)) { :
closing unused connection 8 (https://query1.finance.yahoo.com/v7/finance/download/SGR.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=XAbGyJ59DB.)
Warning in for (i in seq_len(n)) { :
closing unused connection 7 (https://query1.finance.yahoo.com/v7/finance/download/SGR-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=XAbGyJ59DB.)
Warning in for (i in seq_len(n)) { :
closing unused connection 6 (https://query2.finance.yahoo.com/v7/finance/download/SGR-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=qOaFXwzm/vq)
Warning in for (i in seq_len(n)) { :
closing unused connection 5 (https://query2.finance.yahoo.com/v7/finance/download/SIA.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=6YpCtzUSH5v)
Warning in for (i in seq_len(n)) { :
closing unused connection 4 (https://query2.finance.yahoo.com/v7/finance/download/SIA-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=6YpCtzUSH5v)
Warning in for (i in seq_len(n)) { :
closing unused connection 3 (https://query2.finance.yahoo.com/v7/finance/download/SIA-UN.TO?period1=1167609600&period2=1604448000&interval=1d&events=history&crumb=zEEkX.Nkr/X)print(paste("total download time :", Sys.time() - start.time, "s"))[1] "total download time : 1.76225287914276 s"Check downloads
downloaded <- list()
stock_series <- list()
j = 1
k = 1
for (i in 1:length(get_stock_series)) {
if (class(get_stock_series[[i]]) == "try-error") {
#missing_data_log[[k]] <- get_stock_series[[i]]
print(get_stock_series[[i]])
downloaded[[i]] <- FALSE
} else {
downloaded[[i]] <- TRUE
stock_series[[j]] <- get_stock_series[[i]]
j <- j + 1
}
}[1] "Error in getSymbols.yahoo(Symbols = \"BPO.TO\", env = <environment>, verbose = FALSE, : \n Unable to import “BPO.TO”.\nBPO.TO download failed after two attempts. Error message:\nHTTP error 404.\n"
attr(,"class")
[1] "try-error"
attr(,"condition")
<simpleError in getSymbols.yahoo(Symbols = "BPO.TO", env = <environment>, verbose = FALSE, warnings = TRUE, auto.assign = FALSE, .has1sym. = TRUE): Unable to import “BPO.TO”.
BPO.TO download failed after two attempts. Error message:
HTTP error 404.>
[1] "Error in getSymbols.yahoo(Symbols = \"ERE.TO\", env = <environment>, verbose = FALSE, : \n Unable to import “ERE.TO”.\nERE.TO download failed after two attempts. Error message:\nHTTP error 404.\n"
attr(,"class")
[1] "try-error"
attr(,"condition")
<simpleError in getSymbols.yahoo(Symbols = "ERE.TO", env = <environment>, verbose = FALSE, warnings = TRUE, auto.assign = FALSE, .has1sym. = TRUE): Unable to import “ERE.TO”.
ERE.TO download failed after two attempts. Error message:
HTTP error 404.>
[1] "Error in getSymbols.yahoo(Symbols = \"SGR.TO\", env = <environment>, verbose = FALSE, : \n Unable to import “SGR.TO”.\nSGR.TO download failed after two attempts. Error message:\nHTTP error 404.\n"
attr(,"class")
[1] "try-error"
attr(,"condition")
<simpleError in getSymbols.yahoo(Symbols = "SGR.TO", env = <environment>, verbose = FALSE, warnings = TRUE, auto.assign = FALSE, .has1sym. = TRUE): Unable to import “SGR.TO”.
SGR.TO download failed after two attempts. Error message:
HTTP error 404.>real_estate['downloaded'] <- unlist(list(downloaded))A small amount of stocks were not downloaded/are not available
table(real_estate$downloaded)
FALSE TRUE
3 46 stock_series <- bind_rows(stock_series)```r
real_estate[ !real_estate$downloaded, ]
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<!-- rnb-chunk-end -->
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add sub-sector information to stocks
<!-- rnb-text-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3RvY2tfc2VyaWVzIDwtIG1lcmdlKHN0b2NrX3NlcmllcywgcmVhbF9lc3RhdGUsIGJ5Lng9XCJzdG9ja19uYW1lX3Jvb3RcIiwgYnkueT1cInJvb3RfX3RpY2tlclwiLCBhbGw9RkFMU0UpXG5oZWFkKHN0b2NrX3NlcmllcywgMilcbmBgYCJ9 -->
```r
stock_series <- merge(stock_series, real_estate, by.x="stock_name_root", by.y="root__ticker", all=FALSE)
head(stock_series, 2)table(real_estate[real_estate$downloaded, ]$real_estate_sub_sector)
Diversified Industrial/Office/Retail/Residential Real Estate Development and Services
7 30 3
Specialized
6 Most companies go back to 2007 but those with the largest market cap go back only to 2012. Fair amount of distribution.
stock_series_stats_div <- stock_series %>%
filter(real_estate_sub_sector=="Diversified") %>%
group_by(stock_name_root) %>%
summarize(
min = min(date, na.rm=TRUE),
max = max(date, na.rm=TRUE),
real_estate_sub_sector = unique(real_estate_sub_sector),
market_cap__c____30_september_2020 =
max(market_cap__c____30_september_2020),
name = unique(name),
.groups = "keep"
)
stock_series_stats_div$market_cap_fraction <-
stock_series_stats_div$market_cap__c____30_september_2020/sum(stock_series_stats_div$market_cap__c____30_september_2020)
stock_series_stats_div$real_estate_sub_sector <- NULL
stock_series_stats_divp <- ggplot(data = filter(stock_series, real_estate_sub_sector == "Diversified")) +
geom_line(aes(x = as.Date(date), y = returns, group=stock_name_root, color=name, linetype=name)) +
labs(title = "Category - Diversified",
x = "reference month", y = "monthly stock returns",
group= "Ticker", color= "Ticker")
ggplotly(p, width=880, height=450)`group_by_()` is deprecated as of dplyr 0.7.0.
Please use `group_by()` instead.
See vignette('programming') for more help
[90mThis warning is displayed once every 8 hours.[39m
[90mCall `lifecycle::last_warnings()` to see where this warning was generated.[39mTo calculate the index for the category I move the stock tickers into theor own column which allows easier calculations later on. Each row corresponds to one unique month
stock_series_div <- tidyr::pivot_wider(filter(stock_series, real_estate_sub_sector == "Diversified")[, c("date", "stock_name_root", "returns")],
names_from = stock_name_root, values_from = returns)
# ensures that if stock is not available this month, there is a 0
stock_series_div[is.na(stock_series_div)] <- 0
head(stock_series_div, 2)# get market caps for stock tickers of category.
market_cap <- stock_series_stats_div$market_cap__c____30_september_2020
create_index_value <- function(x) {
# create sum of market cap for all stocks existing this month
missing_value_flag <- x != 0
monthly_norm_factor <- sum(market_cap[missing_value_flag])
# index are stocks x weighted by normalized market cap
monthly_index <- sum((x * market_cap) / monthly_norm_factor)
monthly_index
}
stock_series_div$index <- apply(stock_series_div[, stock_series_stats_div$stock_name_root], 1, create_index_value)head(stock_series_div)p <- ggplot(data = stock_series_div) +
geom_line(aes(x = as.Date(date), y = index)) +
labs(title = " Category -Diversified - Index",
x = "reference month", y = "index",
group= "Ticker", color= "Ticker")
ggplotly(p, width=880, height=450)head(stock_series_div)filter(stock_series, real_estate_sub_sector == "Diversified")The companies with a largest market capitalization exist also the longest. If we would weight according to capitalization, then only two companies would dominate an categorical indicator.
stock_series_stats_spec <- stock_series %>%
filter(real_estate_sub_sector=="Specialized") %>%
group_by(stock_name_root) %>%
summarize(
min = min(date, na.rm=TRUE),
max = max(date, na.rm=TRUE),
real_estate_sub_sector = unique(real_estate_sub_sector),
market_cap__c____30_september_2020 =
max(market_cap__c____30_september_2020),
name = unique(name),
.groups = "keep"
)
stock_series_stats_spec$market_cap_fraction <-
stock_series_stats_spec$market_cap__c____30_september_2020/sum(stock_series_stats_spec$market_cap__c____30_september_2020)
stock_series_stats_spec$real_estate_sub_sector <- NULL
stock_series_stats_specp <- ggplot(data = filter(stock_series, real_estate_sub_sector == "Specialized")) +
geom_line(aes(x = as.Date(date), y = returns, group=stock_name_root, color=name, linetype=name)) +
labs(title = "Category - Specialized",
x = "reference month", y = "close stock value",
group= "Ticker", color= "Ticker")
ggplotly(p, width=80, height=450)All 3 companies exist for a longer time at the stock market and have a similar market cap. One company would dominate a categorical indicator with 50%.
stock_series_stats_real <- stock_series %>%
filter(real_estate_sub_sector=="Real Estate Development and Services") %>%
group_by(stock_name_root) %>%
summarize(
min = min(date, na.rm=TRUE),
max = max(date, na.rm=TRUE),
real_estate_sub_sector = unique(real_estate_sub_sector),
market_cap__c____30_september_2020 =
max(market_cap__c____30_september_2020),
name = unique(name),
.groups = "keep"
)
stock_series_stats_real$market_cap_fraction <-
stock_series_stats_real$market_cap__c____30_september_2020/sum(stock_series_stats_real$market_cap__c____30_september_2020)
stock_series_stats_real$real_estate_sub_sector <- NULL
stock_series_stats_realp <- ggplot(data = filter(stock_series, real_estate_sub_sector == "Real Estate Development and Services")) +
geom_line(aes(x = as.Date(date), y = returns, group=stock_name_root, color=name, linetype=name)) +
labs(title = "Category - Real Estate Development and Services",
x = "reference month", y = "close stock value",
group= "Ticker", color= "Ticker")
ggplotly(p, width=780, height=450)stock_series_stats_ind <- stock_series %>%
filter(real_estate_sub_sector=="Industrial/Office/Retail/Residential") %>%
group_by(stock_name_root) %>%
summarize(
min = min(date, na.rm=TRUE),
max = max(date, na.rm=TRUE),
real_estate_sub_sector = unique(real_estate_sub_sector),
market_cap__c____30_september_2020 =
max(market_cap__c____30_september_2020),
.groups = "keep"
)
stock_series_stats_ind$market_cap_fraction <-
stock_series_stats_ind$market_cap__c____30_september_2020/sum(stock_series_stats_ind$market_cap__c____30_september_2020)
stock_series_stats_ind$real_estate_sub_sector <- NULL
stock_series_stats_indTop companies in company for market cap indicates that the market cap is fairly distributed and there may be a less dependency of a categorical indicator on individual companies.
head(stock_series_stats_ind %>% arrange(across(market_cap_fraction, desc)), 10)p <- ggplot(data = filter(stock_series, real_estate_sub_sector == "Industrial/Office/Retail/Residential")) +
geom_line(aes(x = as.Date(date), y = returns, group=stock_name_root, color=name, linetype=name)) +
labs(title = "Category - Industrial/Office/Retail/Residential",
x = "reference month", y = "close stock value",
group= "Ticker", color= "Ticker")
ggplotly(p, width=880, height=450)stock_series_stats_all <- stock_series %>%
group_by(stock_name_root) %>%
summarize(
min = min(date, na.rm=TRUE),
max = max(date, na.rm=TRUE),
real_estate_sub_sector = unique(real_estate_sub_sector),
market_cap__c____30_september_2020 =
max(market_cap__c____30_september_2020),
.groups = "keep"
)
stock_series_stats_all$market_cap_fraction <-
stock_series_stats_all$market_cap__c____30_september_2020/sum(stock_series_stats_all$market_cap__c____30_september_2020)
stock_series_stats_all$real_estate_sub_sector <- NULL
stock_series_stats_allFairly well distributed.
head(stock_series_stats_all %>% arrange(across(market_cap_fraction, desc)), 10)p <- ggplot(data = stock_series) +
geom_line(aes(x = as.Date(date), y = returns, group=stock_name_root, alpha=0.2)) +
labs(title = "All tickers",
x = "reference month", y = "close stock value",
group= "Ticker", color= "Ticker")
ggplotly(p, width=880, height=450)